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Accounting for No-Shows in Experimental Evaluation Designs
Abstract
This article examines how to estimate the effect of a program in the presence of no- shows—persons who are assigned to the program but do not participate. The article briefly discusses the methodological problems involved, describes two current experimental evaluations that are subject to these problems, presents several estimators that overcome these problems, outlines the conditions necessary for these estimators to be feasible, and describes two extensions of the analysis that illustrate a potentially broad range of further applications.
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“One analytical approach to accounting for incomplete adoption is to use Complier Average Causal Effect (commonly known as CACE) modeling (3, 9, 60).”
From An Overview of Research and Evaluation Designs for Dissemination and Implementation (2017) · cites this paper as [9]
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Modern Regression Discontinuity Analysis
Bloom, H. S. (2012) · Journal of Research on Educational Effectiveness · doi:10.1080/19345747.2011.578707 · Cited by 110
“For example, Somers, Zhu, Jacob, and Bloom (2009) have been exploring possibilities to combine regression discontinuity analysis with comparative interrupted time-series analysis to estimate the effects of Reading First on student outcomes for schools in Kentucky. Given the limited statistical precision of regression…”
BackgroundInfluentialSelf-citationTeaching Statistical Inference for Causal Effects in Experiments and Observational Studies
Rubin, D. B. (2004) · Journal of Educational and Behavioral Statistics · doi:10.3102/10769986029003343 · Cited by 108
“The IVE has been reinvented several times despite its old history (e.g., Bloom, 1984; Zelen, 1979).”
BackgroundLearning about Treatment Effects from Experiments with Random Assignment of Treatments
Manski, C. F. (1996) · The Journal of Human Resources · doi:10.2307/146144 · Cited by 103
Michalopoulos, C., Robins, P. K., & Card, D. (2004) · Journal of Public Economics · doi:10.1016/j.jpubeco.2002.02.001 · Cited by 99
Urban Poverty and Educational Outcomes
Ludwig, J., Ladd, H. F., & Duncan, G. J. (2001) · Brookings-Wharton papers on urban affairs · doi:10.1353/urb.2001.0010 · Cited by 100
Moving to Opportunity in Boston: Early Results of a Randomized Mobility Experiment
Katz, L., Kling, J. R., & Liebman, J. B. (2000) · National Bureau of Economic Research · doi:10.3386/w7973 · Cited by 108
Jacob, R. T., Goddard, R. D., Kim, M., Miller, R. J., & Goddard, Y. L. (2014) · Educational Evaluation and Policy Analysis · doi:10.3102/0162373714549620 · Cited by 123
“We therefore also estimate what is often referred to as the effect of the “treatment on the treated,” using a correction suggested by Bloom (1984), in which the intent to treat estimate is scaled up by the proportion of treatment principals who actually participated in the program.”
MethodNational Job Corps Study: The Impacts of Job Corps on Participants' Employment and Related OutcomesPreprint
Schochet, P. Z., Burghardt, J., & Glazerman, S. (2001) · RePEc: Research Papers in Economics · Cited by 99
Managing attrition in clinical research
Flick, S. N. (1988) · Clinical Psychology Review · doi:10.1016/0272-7358(88)90076-1 · Cited by 98
Bayer, A., Grossman, J. B., & DuBois, D. L. (2015) · Journal of Community Psychology · doi:10.1002/jcop.21693 · Cited by 96
“who successfully connected with their assigned mentors (Bloom, 1984; Imbens & Angrist, 1994).”
InfluentialLeventhal, T., Fauth, R. C., & Jeanne Brooks-gunn, B. (2005) · Developmental Psychology · doi:10.1037/0012-1649.41.6.933 · Cited by 96
“Such analyses are referred to as treatment-on-treated program effects and represent a more novel approach to examining randomized clinical trials (Angrist, Imbens, & Rubin, 1996; Bloom, 1984; Katz, Kling, & Liebman, 2001).”
MethodFrangakis, C. (2002) · Biostatistics · doi:10.1093/biostatistics/3.2.147 · Cited by 94
The impact of pensions on health and wellbeing in rural South Africa: Does gender matter?
Schatz, E. J., Gómez-Olivé, F. X., Ralston, M. L., MENKEN, J. A., & Tollman, S. M. (2012) · Social Science & Medicine · doi:10.1016/j.socscimed.2012.07.004 · Cited by 93
Kling, J. R., Ludwig, J., & Katz, L. F. (2004) · National Bureau of Economic Research · doi:10.3386/w10777 · Cited by 93
Landau, S., & Stahl, D. R. (2012) · Statistical Methods in Medical Research · doi:10.1177/0962280212439578 · Cited by 101
Assisting the Transition from Workfare to Work: A Randomized Experiment
Galasso, E., Ravallion, M. D., & Salvia, A. (2004) · Industrial and Labor Relations Review · doi:10.1177/001979390405800107 · Cited by 90
The Long-Term Effects of Moving to Opportunity on Youth OutcomesPreprint
Gennetian, L. A., Sciandra, M., Sanbonmatsu, L., Ludwig, J., Katz, L. F., Duncan, G. J., Kling, J. R., & Kessler, R. C. (2012) · RePEc: Research Papers in Economics · Cited by 90
Wood, R. G., McConnell, S., Moore, Q., Clarkwest, A., & Hsueh, J. (2012) · Journal of Policy Analysis and Management · doi:10.1002/pam.21608 · Cited by 89
Baker, S. G. (1998) · Journal of the American Statistical Association · doi:10.1080/01621459.1998.10473749 · Cited by 88
“ture explores adjusting for all-or-none compliance when the endpoint occurs a certain time after randomization (Angrist, Imbens, and Rubin 1996; Baker 1998; Baker and Lindeman 1994; Bloom 1984; Connor, Prorok, and Weed 1991; Cuzick, Edwards, and Segnan 1997; Glasziou 1992; Ihm 1981; Imbens and Angrist 1994; Newcombe 1…”
BackgroundCheng, L., Liu, H., Zhang, Y., & Zhao, Z. (2017) · Journal of Population Economics · doi:10.1007/s00148-017-0655-y · Cited by 87
“185 of the FE-IV estimate in Table 4) (Bloom, 1984).”
InfluentialCluster randomized trials with treatment noncompliance.
Jo, B., Asparouhov, T., Muthén, B. O., Ialongo, N. S., & Brown, C. H. (2008) · Psychological Methods · doi:10.1037/1082-989x.13.1.1 · Cited by 83
“…effect) estimation have been developed to better estimate treatment efficacy taking into account noncompliance (e.g., Angrist, Imbens & Rubin, 1996; Bloom, 1984; Frangakis & Rubin, 1999; Goetghebeur & Molenberghs, 1996; Hirano, Imbens, Rubin, & Zhou, 2000; Imbens & Rubin, 1997; Jo, 2002a; Little &…”
BackgroundMethodCan Small High Schools of Choice Improve Educational Prospects for Disadvantaged Students?
Bloom, H. S., & Unterman, R. (2014) · Journal of Policy Analysis and Management · doi:10.1002/pam.21748 · Cited by 86
Self-citationSciandra, M., Sanbonmatsu, L., Duncan, G. J., Gennetian, L. A., Katz, L. F., Kessler, R. C., Kling, J. R., & Ludwig, J. (2013) · Journal of Experimental Criminology · doi:10.1007/s11292-013-9189-9 · Cited by 86
Machine + Man: A Field Experiment on the Role of Discretion in Augmenting AI-Based Lending Models
Costello, A. M., Down, A. K., & Mehta, M. N. (2019) · Journal of Accounting and Economics · doi:10.1016/j.jacceco.2020.101360 · Cited by 88
Analyzing a randomized trial on breast self-examination with noncompliance and missing outcomes
Mealli, F. (2004) · Biostatistics · doi:10.1093/biostatistics/5.2.207 · Cited by 85
“…researchers have also focused on the ITT effect for the subpopulation of compliers (units who always comply with their treatment assignment) (Bloom, 1984; Sommer and Zeger, 1991; Imbens and Angrist, 1994; Angristet al., 1996; Imbens and Rubin, 1997b; Baker, 1998, 2000; Little and Yau, 1998).”
BackgroundPelletier, K. R. (1999) · American Journal of Health Promotion · doi:10.4278/0890-1171-13.6.333 · Cited by 85
Treatment Effect Heterogeneity in Theory and PracticePreprint
Angrist, J. D. (2003) · SSRN Electronic Journal · doi:10.2139/ssrn.432501 · Cited by 85
Long-Term Neighborhood Effects on Low-Income Families: Evidence from Moving to Opportunity
Ludwig, J., Duncan, G. J., Gennetian, L. A., Katz, L. F., Kessler, R. C., Kling, J. R., & Sanbonmatsu, L. (2013) · National Bureau of Economic Research · doi:10.3386/w18772 · Cited by 95
Calcagno, J. C., & Long, B. T. (2008) · Cited by 84
Dynamics of Performance-Measurement Systems
Courty, P. (2003) · Oxford Review of Economic Policy · doi:10.1093/oxrep/19.2.268 · Cited by 86
“We use a regression framework to estimate the earnings impacts, employing a method suggested by Bloom (1984) to control for dropouts.”
BackgroundMethodInfluentialBeyond intent to treat (ITT): A complier average causal effect (CACE) estimation primer
Peugh, J., Strotman, D., McGrady, M. E., Rausch, J. R., & Kashikar‐Zuck, S. (2016) · Journal of School Psychology · doi:10.1016/j.jsp.2015.12.006 · Cited by 85
Elango, S., García, J. L., Heckman, J. J., & Hojman, A. (2015) · National Bureau of Economic Research · doi:10.3386/w21766 · Cited by 80
Masked intervention effects: Analytic methods for addressing low dosage of intervention
Lochman, J. E., Boxmeyer, C. L., Powell, N. P., Roth, D. L., & Windle, M. T. (2006) · New Directions for Evaluation · doi:10.1002/ev.184 · Cited by 80
Preventing Youth Violence and Dropout: A Randomized Field Experiment
Heller, S., Pollack, H., Ander, R., & Ludwig, J. (2013) · doi:10.3386/w19014 · Cited by 79
“…report the effect of participating in the program for those who actually participate, which we estimate using two-stage least squares with random assignment (Z is ) as an instrumental variable (IV) for participation (P ist ), as in equations (2) and (3) (Angrist, Imbens & Rubin 1996; Bloom 1984).”
Sabol, T. J., & Chase-Lansdale, P. L. (2014) · Journal of Policy Analysis and Management · doi:10.1002/pam.21799 · Cited by 79
Peer Influence in the Diffusion of iPhone 3G over a Large Social Network
de Matos, M. G., Ferreira, P. A., & Krackhardt, D. (2014) · MIS Q. · doi:10.25300/misq/2014/38.4.08 · Cited by 71
Gay, C. (2012) · Urban Affairs Review · doi:10.1177/1078087411426399 · Cited by 95
(2010) · PsycEXTRA Dataset · doi:10.1037/e568062013-001 · Cited by 77
Program Applicants as a Comparison Group in Evaluating Training Programs: Theory and a Test
Bell, S. H., Orr, L. L., Blomquist, J. D., & Cain, G. G. (1995) · Upjohn Research (W.E. Upjohn Institute for Employment Research) · doi:10.17848/9780585284545 · Cited by 73
Angelucci, M., & Attanasio, O. P. (2013) · American Economic Journal Economic Policy · doi:10.1257/pol.5.1.146 · Cited by 74
Causal inference from indirect experiments
Pearl, J. (1995) · Artificial Intelligence in Medicine · doi:10.1016/0933-3657(95)00027-3 · Cited by 79
McAuliffe, W. E. (1990) · Journal of Psychoactive Drugs · doi:10.1080/02791072.1990.10472544 · Cited by 75
Not Too Late: Improving Academic Outcomes among Adolescents
Guryan, J., Ludwig, J., Bhatt, M. P., Cook, P. J., Davis, J. M. V., Dodge, K., Farkas, G., Fryer, R. G., Mayer, S., Pollack, H., et al. (2023) · American Economic Review · doi:10.1257/aer.20210434 · Cited by 77
Kemple, J. J. (2001) · Cited by 73
Jo, B. (2002) · Statistics in Medicine · doi:10.1002/sim.1267 · Cited by 72
“A previous study [14] using the instrumental variable approach [2], which normally yields a very similar outcome to CACE analysis, showed that the choice of threshold made little di erence in the JOBS Intervention Study, possibly because individuals assigned to the intervention condition either attended most sessions…”
ResultTrimming for Bounds on Treatment Effects with Missing Outcomes
Lee, D. (2002) · National Bureau of Economic Research · doi:10.3386/t0277 · Cited by 72
“…1997; Heckman and Vytlacil 1999, 2000a, 2000b) have constructed such bounds to address a different problem – that of imperfect compliance of the treatment, even when “intention” to treat is effectively randomized (Bloom 1984; Robins 1989; Imbens and Angrist, 1994; Angrist, Imbens, and Rubin 1996).”
BackgroundAmerican Education Research Changes Tack
Angrist, J. D. (2004) · Oxford Review of Economic Policy · doi:10.1093/oxrep/grh011 · Cited by 70
Improving Educational Outcomes for Poor Children
Jacob, B. A., & Ludwig, J. (2008) · National Bureau of Economic Research · doi:10.3386/w14550 · Cited by 77
Evaluation of the DC Opportunity Scholarship Program: Impacts After Three Years
Wolf, P. J., Gutmann, B., Puma, M. J., Kisida, B., Rizzo, L., Eissa, N., & Silverberg, M. K. (2007) · Journal of the Arkansas Academy of Science · Cited by 70
Oportunidades: Program Effect on Consumption, Low Participation, and Methodological Issues
Angelucci, M., & Attanasio, O. P. (2009) · Economic Development and Cultural Change · doi:10.1086/596616 · Cited by 69
“We identify the ATT using a conditional version of the Local Average Treatment Effect (Angrist, Imbens, and Rubin 1996) and of the Bloom estimator (Bloom 1984, and Heckman 1996), where the availability of the treatment is not random, unlike in the other papers mentioned.”
MethodSubgroup Analysis in Social Experiments: Measuring Program Impacts Based on Post-Treatment Choice
Peck, L. R. (2003) · American Journal of Evaluation · doi:10.1016/s1098-2140(03)00031-6 · Cited by 69
Treatment noncompliance in randomized experiments: Statistical approaches and design issues.
Sagarin, B. J., West, S. G., Ratnikov, A., Homan, W. K., Ritchie, T. D., & Hansen, E. J. (2014) · Psychological Methods · doi:10.1037/met0000013 · Cited by 69
Assessing the sensitivity of methods for estimating principal causal effects
Stuart, E. A., & Jo, B. (2011) · Statistical Methods in Medical Research · doi:10.1177/0962280211421840 · Cited by 71
Weiss, M. J., Ratledge, A., Sommo, C., & Gupta, H. (2019) · American Economic Journal Applied Economics · doi:10.1257/app.20170430 · Cited by 68
Rhodes, W. M., Pelissier, B. M. M., Gaes, G. G., Saylor, W. G., Camp, S. D., & Wallace, S. (2001) · Evaluation Review · doi:10.1177/0193841x0102500303 · Cited by 68
Does Head Start Do Any Lasting Good?
Gibbs, C. R., Ludwig, J., & Miller, D. L. (2011) · National Bureau of Economic Research · doi:10.3386/w17452 · Cited by 67
Halting the Summer Achievement Slide: A Randomized Field Trial of the KindergARTen Summer Camp
Borman, G. D., Goetz, M. E., & Dowling, N. M. (2009) · Journal of Education for Students Placed at Risk (JESPAR) · doi:10.1080/10824660802427652 · Cited by 66
“Fortunately, as Bloom (1984) suggested, in some circumstances it is possible to infer this impact.”
BackgroundInfluentialTurney, K., Edin, K. J., Clampet‐Lundquist, S., Kling, J. R., & Duncan, G. J. (2006) · Brookings-Wharton papers on urban affairs · doi:10.1353/urb.2006.0028 · Cited by 64
Causal inference from randomized trials in social epidemiology
Kaufman, J. S., Kaufman, S., & Poole, C. (2003) · Social Science & Medicine · doi:10.1016/s0277-9536(03)00135-7 · Cited by 64
“that is, simply the ITT estimate divided by the proportion moving among those offered a voucher (Bloom, 1984).”
BackgroundThe Long-Term Effects of Moving to Opportunity on Adult Health and Economic Self-Sufficiency
Sanbonmatsu, L., Marvakov, J., Potter, N. A., Yang, F., Adam, E. K., Congdon, W. J., Duncan, G. J., Gennetian, L. A., Katz, L. F., Kling, J. R., et al. (2012) · Digital Access to Scholarship at Harvard (DASH) (Harvard University) · Cited by 63
Visher, M. G., Weiss, M. J., Weissman, E., Rudd, T., & Wathington, H. D. (2012) · Cited by 63
Bloom, H. S., Thompson, S. L., & Unterman, R. (2010) · SSRN Electronic Journal · Cited by 63
Self-citationThe Impacts of Regular Upward Bound: Results from the Third Follow-Up Data CollectionPreprint
Myers, D. E., Olsen, R. B., Seftor, N. S., Young, J. A., & Tuttle, C. C. (2004) · RePEc: Research Papers in Economics · Cited by 61
Pierre, R. G. S., Ricciuti, A. E., & Rimdzius, T. (2005) · Developmental Psychology · doi:10.1037/0012-1649.41.6.953 · Cited by 60
Identification of Causal Effects Using Instrumental Variables: Rejoinder
Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996) · Journal of the American Statistical Association · doi:10.2307/2291634 · Cited by 66
Graphical Models for Quasi-experimental Designs
Steiner, P. M., Kim, Y., Hall, C. E., & Su, D. (2015) · Sociological Methods & Research · doi:10.1177/0049124115582272 · Cited by 56
“, there are no always takers such that noncompliance is only due to never takers), one can show that the ATE on the treated (ATT) is identified (Bloom 1984; Frölich and Melly 2008).”
BackgroundEarly motherhood and later partnerships
Ermisch, J., & Pevalin, D. J. (2005) · Journal of Population Economics · doi:10.1007/s00148-004-0216-z · Cited by 56
“This is because, in treatment effect terminology, there are no ‘always-takers’ (i.e., those who have a birth regardless of the value of the instrument (occurrence of a miscarriage) – see Bloom (1984) and Angrist (2004)).”
BackgroundCowen, J. M. (2008) · Policy Studies Journal · doi:10.1111/j.1541-0072.2008.00268.x · Cited by 54
Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT
Donald, S. G., Hsu, Y.-C., & Lieli, R. P. (2014) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2014.888290 · Cited by 54
“Assumption 3 dates back to (at least) Bloom (1984), who estimated the effect of a program in the presence of “no shows.””
BackgroundBell, S. H., & Orr, L. L. (1994) · The Journal of Human Resources · doi:10.2307/146055 · Cited by 54
Paola, M. D., Scoppa, V., & Nisticò, R. (2012) · Journal of Human Capital · doi:10.1086/664795 · Cited by 55
“*** Statistically significant at the 1 percent level. or may not be interested in it (see Bloom 1984; Angrist and Pischke 2009).”
BackgroundMiller, R. J., Goddard, R. D., Kim, M., Jacob, R. T., Goddard, Y. L., & Schroeder, P. (2016) · Educational Administration Quarterly · doi:10.1177/0013161x16651926 · Cited by 52
“We therefore also estimated what is often referred to as the effect of the treatment on the treated (TOT), using a correction suggested by Bloom (1984), in which the ITT estimate is scaled up by the proportion of principals who fully participated in the program.”
MethodAlmost random: Evaluating a large-scale randomized nutrition program in the presence of crossover
Linnemayr, S., & Alderman, H. (2010) · Journal of Development Economics · doi:10.1016/j.jdeveco.2010.06.002 · Cited by 52
Peng, Y., Little, R. J. A., & Raghunathan, T. E. (2004) · Biometrics · doi:10.1111/j.0006-341x.2004.00208.x · Cited by 52
“…of subjects who would comply with either of the treatments if assigned to them, using the randomization indicator as an instrumental variable (Bloom, 1984; Sommer and Zeger, 1991; Goetghebeur and Shapiro, 1996; Imbens and Rubin, 1996, 1997; Baker, 1998; Frangakis and Rubin, 1999, 2002; Yau…”
James‐Burdumy, S., Dynarski, M., & Deke, J. (2007) · Educational Evaluation and Policy Analysis · doi:10.3102/0162373707309077 · Cited by 52
“To estimate impacts of the “treatment on the treated,” or the program’s impact on participating students (92% of students in the treatment group attended centers for at least 1 day), the study used a two-stage instrumental variables procedure in which treatment status is used as an instrument for participation in the…”
BackgroundMethodMills, G., Gale, W. G., Patterson, R., Engelhardt, G. V., Eriksen, M. D., & Apostolov, E. (2008) · Journal of Public Economics · doi:10.1016/j.jpubeco.2007.09.014 · Cited by 94
Pelletier, K. R. (1997) · Journal of Occupational and Environmental Medicine · doi:10.1097/00043764-199712000-00009 · Cited by 51
EARLY HEAD START HOME VISITATION: THE ROLE OF IMPLEMENTATION IN BOLSTERING PROGRAM BENEFITS
Harden, B. J., Chazan‐Cohen, R., Raikes, H., & Vogel, C. A. (2012) · Journal of Community Psychology · doi:10.1002/jcop.20525 · Cited by 49
Torgesen, J. K., Myers, D., Schirm, A. L., Stuart, E. A., Vartivarian, S., Mansfield, W., Stancavage, F., Durno, D., Javorsky, R., & Haan, C. (2006) · National Center for Education Evaluation and Regional Assistance · Cited by 48
Olsho, L. E. W., Klerman, J. A., Ritchie, L. D., Wakimoto, P., Webb, K., & Bartlett, S. (2015) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2014.12.026 · Cited by 47
From Job Loss to Reemployment: Field Experiments in Prevention-Focused Coping
Caplan, R. D., Vinokur, A. D., & Price, R. (1997) · Sage eBooks · doi:10.4135/9781452243801.n15 · Cited by 47
“The second type of analysis compared those who actually did show up and complete the intervention program with persons in the control group who were similar to them and, therefore, would be likely to show up were they allowed to participate in the intervention (Bloom, 1984; Vinokur et al., 1991).”
ResultStatistical Analysis for Multisite Trials Using Instrumental Variables With Random Coefficients
Raudenbush, S. W., Reardon, S. F., & Nomi, T. (2012) · Journal of Research on Educational Effectiveness · doi:10.1080/19345747.2012.689610 · Cited by 46
Assisting the Transition from Workfare to Work: A Randomized Experiment
Galasso, E., Ravallion, M. D., & Salvia, A. (2004) · Industrial and Labor Relations Review · doi:10.2307/4126640 · Cited by 46
Neighborhood effects on use of African-American Vernacular English
Rickford, J. R., Duncan, G. J., Gennetian, L. A., Gou, R. Y., Greene, R. D., Katz, L. F., Kessler, R. C., Kling, J. R., Sanbonmatsu, L., Sánchez-Ordoñez, A. E., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1500176112 · Cited by 50
Child Health and Neighborhood Conditions
Fortson, J. G., & Sanbonmatsu, L. (2010) · The Journal of Human Resources · doi:10.1353/jhr.2010.0025 · Cited by 46
“…treatment group noncompliers), then the TOT effect should be the ITT effect divided by the proportion of compliers in the relevant treatment group (Bloom 1984).12 In addition to looking at the overall effects of treatment group assignment, we can also look to see whether any effects differ by age…”
BackgroundHandling Missing Data in Randomized Experiments with Noncompliance
Jo, B., Ginexi, E. M., & Ialongo, N. S. (2010) · Prevention Science · doi:10.1007/s11121-010-0175-4 · Cited by 46
“In principle, it is possible to incorporate covariates in the instrumental variable framework (Bloom, 1984; Little & Yau, 1998).”
The Impact of Oportunidades on Consumption, Savings and Transfers*
Angelucci, M., Attanasio, O. P., & Maro, V. D. (2012) · Fiscal Studies · doi:10.1111/j.1475-5890.2012.00163.x · Cited by 45
“This estimator is a conditional version of the Bloom estimator (Bloom, 1984; Heckman, 1996), where the availability of the treatment is not random, unlike in the other papers mentioned.”
MethodAGE-Learn: Ontology-Based Representation of Personalized Gamification in E-learning
Bennani, S., Maalel, A., & Ghézala, H. B. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.09.096 · Cited by 45
The effects of housing and neighborhood conditions on child mortality
Jacob, B. A., Ludwig, J., & Miller, D. L. (2012) · Journal of Health Economics · doi:10.1016/j.jhealeco.2012.10.008 · Cited by 44
“…family leases up with a housing voucher obtained from any source – either the CHAC lottery or one of the smaller, specialized voucher allocations that occurred during our study period, such as for families whose public housing projects were demolished (Bloom, 1984, Angrist, Imbens and Rubin, 1996).”
Using Cluster Analysis in Program Evaluation
Peck, L. R. (2005) · Evaluation Review · doi:10.1177/0193841x04266335 · Cited by 45
Housing mobility programs and economic self-sufficiency
Ludwig, J., Duncan, G. J., & Pinkston, J. C. (2004) · Journal of Public Economics · doi:10.1016/j.jpubeco.2003.07.010 · Cited by 45
“…the ITT effect divided by the treatment group’s take-up rate if randomization is conducted properly, no control group families are ‘‘treated’’, and assignment to the experimental or Section 8- only groups has no effect on those who do not relocate through MTO (Bloom, 1984; Angrist et al., 1996).”
BackgroundSeftor, N. S., Mamun, A. A., & Schirm, A. L. (2009) · RePEc: Research Papers in Economics · Cited by 45
A General Test for Distortions in Performance Measures
Courty, P., & Marschke, G. (2008) · The Review of Economics and Statistics · doi:10.1162/rest.90.3.428 · Cited by 44
“25 For a comprehensive discussion of the Bloom assumption and of the problem of drop-outs in experimental evaluations more generally, see Heckman, Smith, and Taber (2002).”
InfluentialPrivileging the Participant: The Importance of Sub-Group Analysis in Social Welfare Evaluations
Gibson, C. M. (2003) · American Journal of Evaluation · doi:10.1016/j.ameval.2003.09.002 · Cited by 44
Myers, N. D., Prilleltensky, I., Prilleltensky, O., McMahon, A., Dietz, S., & Rubenstein, C. (2017) · Prevention Science · doi:10.1007/s11121-017-0779-z · Cited by 40
“Model 1 and model 2 employed complier average causal effect (CACE; e.g., Angrist et al. 1996; Bloom 1984) estimation.”
The effect of supplemental instruction on academic performance: An encouragement design experiment
Paloyo, A. R., Rogan, S., & Siminski, P. (2016) · Economics of Education Review · doi:10.1016/j.econedurev.2016.08.005 · Cited by 42
“RED is akin to RCTs with partial or imperfect compliance, where consistent estimates of treatment effects can be recovered by standard instrumental-variable regression techniques [Angrist et al. 1996; Bloom 1984].”
MethodEstimating causal effects : using experimental and observational designs : a think tank white paper
Schneider, B. (2007) · Cited by 42
Effects of government initiatives on youth crime
Sabatés, R., & Feinstein, L. (2007) · Oxford Economic Papers · doi:10.1093/oep/gpm038 · Cited by 41
The Impact of Learning Communities for Students in Developmental Education
Weiss, M. J., Visher, M. G., Weissman, E., & Wathington, H. D. (2015) · Educational Evaluation and Policy Analysis · doi:10.3102/0162373714563307 · Cited by 49
“While researchers often estimate TOT (or local average treatment effect [LATE]) using instrumental variables (IVs) analysis, the assumptions required for such analyses are likely violated in this study (Angrist, Imbens, & Rubin, 1996; H. S. Bloom, 1984).”
MethodWeiss, M. J., Mayer, A. K., Cullinan, D., Ratledge, A., Sommo, C., & Diamond, J. (2014) · Journal of Research on Educational Effectiveness · doi:10.1080/19345747.2014.946634 · Cited by 41
“Such analyses require the assumption that the treatment has no effect on students assigned to the program who did not experience the program, referred to as “no-shows” or “never-takers” (Angrist, Imbens, & Rubin, 1996; Bloom, 1984).”
Background
Metadata
- Authors
- Bloom, H. S.
- Affiliations
- Harvard University (US)
- Corresponding author
- Bloom, Howard S.
- Venue
- Evaluation Review
- Publisher
- SAGE Publications
- Published
- 1984-04
- Volume
- 8
- Issue
- 2
- Pages
- 225-246
- ISSN
- 0145-4692 (ISSN-L)
- 0193-841X
- 1552-3926
- OpenAlex
- W2046165968
- Cited by
- 532
- References
- 2
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 15 · 2023: 9 · 2022: 18 · 2021: 13 · 2020: 15 · 2019: 14 · 2018: 14 · 2017: 17 · 2016: 22 · 2015: 22 · 2014: 29 · 2013: 19 · 2012: 38
- FWCI
- 1.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Evaluation Review (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Public Administration › Evaluation and Performance Assessment
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- missed appointments, program participation, program evaluation, experimental design, estimation bias, non-compliance, treatment effect estimation
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